Systems and methods for providing personalized content

ABSTRACT

Systems, methods, and non-transitory computer-readable media can generate a set of candidate content items from a plurality of content items that are available in the social networking system, wherein one or more of the candidate content items are to be included in a personalized content stream for a first user. A corresponding score for each of the candidate content items can be generated with respect to the first user. A first set of content items can be determined from the set of candidate content items based at least in part on the respective scores, wherein content items in the first set are included in the personalized content stream.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/299,035, filed on Oct. 20, 2016 and entitled “SYSTEMS AND METHODS FORPROVIDING PERSONALIZED CONTENT”, which is incorporated herein byreference in its entirety.

FIELD OF THE INVENTION

The present technology relates to the field of content provision. Moreparticularly, the present technology relates to techniques for providingpersonalized content to users.

BACKGROUND

Today, people often utilize computing devices (or systems) for a widevariety of purposes. Users can use their computing devices to, forexample, interact with one another, access content, share content, andcreate content. In some cases, content items can include postings frommembers of a social network. The postings may include text and mediacontent items, such as images, videos, and audio. The postings may bepublished to the social network for consumption by others.

Under conventional approaches, users may post various content items tothe social networking system. In general, content items posted by afirst user can be included in the respective content feeds of otherusers of the social networking system, for example, that have “followed”the first user. By following (or subscribing to) the first user, some orall content that is produced, or posted, by the first user may beincluded in the respective content feeds of the following users. A userfollowing the first user can simply unfollow the first user to preventnew content that is produced by the first user from being included inthe following user's content feed.

SUMMARY

Various embodiments of the present disclosure can include systems,methods, and non-transitory computer readable media configured togenerate a set of candidate content items from a plurality of contentitems that are available in the social networking system, wherein one ormore of the candidate content items are to be included in a personalizedcontent stream for a first user. A corresponding score for each of thecandidate content items can be generated with respect to the first user.A first set of content items can be determined from the set of candidatecontent items based at least in part on the respective scores, whereincontent items in the first set are included in the personalized contentstream.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to determine a likelihood of the firstuser selecting an option to like a candidate content item through thesocial networking system, the likelihood being determined using atrained machine learning model, wherein the score for the candidatecontent item is based at least in part on the likelihood.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to determine a likelihood of the firstuser watching one or more additional content items after having viewed acandidate content item, the likelihood being determined using a trainedmachine learning model, wherein the score for the candidate content itemis based at least in part on the likelihood.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to determine a likelihood of the firstuser watching a candidate content item to completion, the likelihoodbeing determined using a trained machine learning model, wherein thescore for the candidate content item is based at least in part on thelikelihood.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to determine a likelihood of the firstuser watching a playback of a candidate content item for a duration oftime that is longer than an average duration of time the first userwatches playbacks of content items, the likelihood being determinedusing a trained machine learning model, wherein the score for thecandidate content item is based at least in part on the likelihood.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to determine a likelihood of the firstuser watching a playback of a candidate content item for a duration oftime that is longer than an average duration of time that other userswatched playbacks of the candidate content item, the likelihood beingdetermined using a trained machine learning model, wherein the score forthe candidate content item is based at least in part on the likelihood.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to obtain one or more content items thatwere liked by at least one second user that the first user is followingin the social networking system.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to determine that the first user haspreviously liked one or more content items that were posted by at leastone second user and obtain one or more content items that were liked bythe second user.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to obtain one or more content items thatwere posted by users that are located in a geographic region in whichthe first user is also located or has visited.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to filter the set of candidate contentitems to exclude content items that are likely to be flagged by users asbeing inappropriate or content items that were posted by users that havepreviously been flagged as posters of inappropriate content.

Various embodiments of the present disclosure can include systems,methods, and non-transitory computer readable media configured to obtaininformation describing a personalized content stream of a first user,the personalized content stream including a set of content items to bepresented to the first user according to a first ordering. A secondordering for the set of content items is determined based on one or morecriteria, the second ordering satisfying at least one measure ofconsistency. The personalized content stream is modified to correspondto the second ordering.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to cluster content items in the set basedon one or more criteria, wherein each content item is assigned to acluster in a plurality of clusters and determine an order in which topresent each cluster in the plurality of clusters.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to determine a respective classificationfor each content item in the set and assign each content item in the setto a cluster in the plurality of clusters based on its respectiveclassification.

In some embodiments, the classification of a content item is based onits assigned topic, category, sub-category, subject matterclassification, or visual theme.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to determine a respective geographiclocation for each content item in the set, the geographic locationcorresponding to a geographic location of a user that posted the contentitem and assign each content item in the set to a cluster in theplurality of clusters based on its respective geographic location.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to determine a respective soundcharacteristics for each content item in the set and assign each contentitem in the set to a cluster in the plurality of clusters based on itsrespective sound characteristics.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to determine respective attributesdescribing music that is played during playback of each content item inthe set and assign each content item in the set to a cluster in theplurality of clusters based on the respective attributes describing themusic played during playback of the content item.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to determine respective distance scoresbetween clusters in the plurality of clusters, wherein a distance scorefor a first cluster and a second cluster measures a similarity betweenthe first cluster and the second cluster and generate the order ofclusters in the plurality of clusters based at least in part on therespective distance scores.

In some embodiments, the distance score for the first cluster and thesecond cluster is determined based at least in part on a visualsimilarity between content items in the first cluster and content itemsin the second cluster.

In some embodiments, the distance score for the first cluster and thesecond cluster is determined based at least in part on a social affinitybetween users that posted content items in the first cluster and usersthat posted content items in the second cluster.

It should be appreciated that many other features, applications,embodiments, and/or variations of the disclosed technology will beapparent from the accompanying drawings and from the following detaileddescription. Additional and/or alternative implementations of thestructures, systems, non-transitory computer readable media, and methodsdescribed herein can be employed without departing from the principlesof the disclosed technology.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system including an example contentprovider module, according to an embodiment of the present disclosure.

FIG. 2 illustrates an example personalized content stream module,according to an embodiment of the present disclosure.

FIGS. 3A-B illustrates an example interface, according to an embodimentof the present disclosure.

FIGS. 4A-C illustrate other example interfaces, according to anembodiment of the present disclosure.

FIG. 5 illustrates an example method for providing personalized content,according to an embodiment of the present disclosure.

FIG. 6 illustrates an example method for reordering a personalizedcontent stream, according to an embodiment of the present disclosure.

FIG. 7 illustrates a network diagram of an example system including anexample social networking system that can be utilized in variousscenarios, according to an embodiment of the present disclosure.

FIG. 8 illustrates an example of a computer system or computing devicethat can be utilized in various scenarios, according to an embodiment ofthe present disclosure.

The figures depict various embodiments of the disclosed technology forpurposes of illustration only, wherein the figures use like referencenumerals to identify like elements. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated in the figures can be employedwithout departing from the principles of the disclosed technologydescribed herein.

DETAILED DESCRIPTION Approaches for Providing Personalized Content

People often utilize computing devices (or systems) for a wide varietyof purposes. Users can use their computing devices to, for example,interact with one another, access content, share content, and createcontent. In some cases, content items can include postings from membersof a social network. The postings may include text and media contentitems, such as images, videos, and audio. The postings may be publishedto the social network for consumption by others.

Under conventional approaches, users may post various content items tothe social networking system. In general, content items posted by afirst user can be included in the respective content feeds of otherusers of the social networking system that have “followed” the firstuser. By following (or subscribing to) the first user, some or allcontent that is produced, or posted, by the first user may be includedin the respective content feeds of the users following the first user. Auser following the first user can prevent new content from the firstuser from being included in the user's content feed by simply“unfollowing” the first user. Under conventional approaches, there maybe instances when a user does not follow enough users to result in adesired amount of new content to be included in the user's content feed.In one example, the user may follow a limited number of other users thatpost new content items infrequently. In this example, the user may beleft with a stale content feed once the content items posted by thatlimited number of users have been exhausted, or viewed, by the user. Asa result, the user's continued engagement with the social networkingsystem may be negatively affected.

An improved approach rooted in computer technology overcomes theforegoing and other disadvantages associated with conventionalapproaches specifically arising in the realm of computer technology. Invarious embodiments, users of the social networking system can accesscontent streams that have been personalized for the user. Such contentstreams may include various types of content items that each have beendetermined to be relevant, or of interest, to a given user. In general,a personalized content stream can be composed using a number ofindividual content items that have been posted by various users of thesocial networking system. In various embodiments, the personalizedcontent stream for a user can be continually updated to include newlyposted content items that have been determined to be relevant to theuser. As a result, the personalized content stream can provide ancontinuous stream of relevant various content items that are availablefor the user to browse. In some embodiments, such personalized contentstream can further be customized to improve the user experience. Forexample, the presentation of content items as part of the personalizedcontent stream may be reordered based on topic, theme (e.g., visualtheme, audio theme, etc.), motion, geographic location, and sound, toname some examples.

FIG. 1 illustrates an example system 100 including an example contentprovider module 102, according to an embodiment of the presentdisclosure. As shown in the example of FIG. 1, the content providermodule 102 can include a content module 104, a follow module 106, a likemodule 108, and a personalized content stream module 110. In someinstances, the example system 100 can include at least one data store112. The components (e.g., modules, elements, etc.) shown in this figureand all figures herein are exemplary only, and other implementations mayinclude additional, fewer, integrated, or different components. Somecomponents may not be shown so as not to obscure relevant details.

In some embodiments, the content provider module 102 can be implemented,in part or in whole, as software, hardware, or any combination thereof.In general, a module as discussed herein can be associated withsoftware, hardware, or any combination thereof. In some implementations,one or more functions, tasks, and/or operations of modules can becarried out or performed by software routines, software processes,hardware, and/or any combination thereof. In some cases, the contentprovider module 102 can be implemented, in part or in whole, as softwarerunning on one or more computing devices or systems, such as on a useror client computing device. In one example, the content provider module102 or at least a portion thereof can be implemented as or within anapplication (e.g., app), a program, or an applet, etc., running on auser computing device or a client computing system, such as the userdevice 710 of FIG. 7. In another example, the content provider module102 or at least a portion thereof can be implemented using one or morecomputing devices or systems that include one or more servers, such asnetwork servers or cloud servers. In some instances, the contentprovider module 102 can, in part or in whole, be implemented within orconfigured to operate in conjunction with a social networking system (orservice), such as the social networking system 730 of FIG. 7.

The content provider module 102 can be configured to communicate and/oroperate with the at least one data store 112, as shown in the examplesystem 100. The at least one data store 112 can be configured to storeand maintain various types of data. For example, the data store 112 canstore information describing various content that has been posted byusers of a social networking system. In some implementations, the atleast one data store 112 can store information associated with thesocial networking system (e.g., the social networking system 730 of FIG.7). The information associated with the social networking system caninclude data about users, social connections, social interactions,locations, geo-fenced areas, maps, places, events, pages, groups, posts,communications, content, feeds, account settings, privacy settings, asocial graph, and various other types of data. In some implementations,the at least one data store 112 can store information associated withusers, such as user identifiers, user information, profile information,user specified settings, content produced or posted by users, andvarious other types of user data.

The content provider module 102 can be configured to provide users withaccess to content that is posted through a social networking system. Forexample, the content module 104 can provide a first user with access tocontent items through an interface that is provided by a softwareapplication (e.g., a social networking application) running on acomputing device of the first user. The first user can also interactwith the interface to post content items to the social networkingsystem. Such content items may include text, images, audio, and videos,for example.

In various embodiments, other users of the social networking system canaccess content items posted by the first user. In one example, the otherusers can access the content items by searching for the first userthrough the interface, for example, by user name. In some instances,some users may want to see content items posted by the first user intheir respective content feed. To cause content items posted by thefirst user to be included in their respective content feed, a user canselect an option through the interface to subscribe to, or “follow”, thefirst user. The follow module 106 can process the user's request byidentifying the user as a follower of (or “friend” of) the first user inthe social networking system. As a result, some or all content itemsthat are posted by the first user can automatically be included in therespective content feed of the user. If the user decides that they nolonger want to see content from the first user in their respectivecontent feed, the user can select an option through the interface to“unfollow” the first user. As a result, the follow module 106 can removethe association between the user and the first user so that contentitems posted by the first user are no longer included in the contentfeed of the user. In some instances, the user may want to endorse, or“like”, a content item. In such instances, the user can select an optionprovided in the interface to like the desired content item. The likemodule 108 can determine when a user likes a given content item and canstore information describing this relationship. In some embodiments,this information can be stored in a social graph as described inreference to FIG. 7.

In various embodiments, the personalized content stream module 110 isconfigured to generate customized content streams for users usingcontent items that are available from various sources including anycontent items that are posted through the social networking system. Moredetails regarding the personalized content stream module 110 will beprovided below with reference to FIG. 2.

FIG. 2 illustrates a personalized content stream module 202, accordingto an embodiment of the present disclosure. In some embodiments, thepersonalized content stream module 110 of FIG. 1 can be implemented withthe personalized content stream module 202. As shown in the example ofFIG. 2, the personalized content stream module 202 can include acandidate generation module 204, a filtering module 206, a rankingmodule 208, a continuity module 210, and a music module 212.

In various embodiments, the personalized content stream module 202 cangenerate respective content streams for users. In some embodiments, eachpersonalized content stream is tailored for a given user. That is, thepersonalized content stream will generally include content items thathave been determined to be of interest to the user based, in part, onvarious metrics. As mentioned, such personalized content streams may becomposed using various types of content items (e.g., animated content,videos, etc.) that are posted, or otherwise available, through thesocial networking system.

When generating a personalized content stream for a first user, thecandidate generation module 204 can determine a set of candidate contentitems that are eligible for inclusion in the personalized contentstream. In some embodiments, the candidate generation module 204identifies as candidates any content items that were liked by users thatare being followed by the first user. For example, if a second user thatis being followed by the first user likes a content item, then thatcontent item can be included in the set of candidate content items. Insome embodiments, the candidate generation module 204 identifies ascandidates any content items that were liked by users whose contentitems were previously liked by the first user. For example, if the firstuser liked a first content item that was posted by a second user and thesecond user likes a second content item, then the second content itemcan be included in the set of candidate content items. In someembodiments, the candidate generation module 204 identifies ascandidates any content items that were posted by users that werereferenced in one or more search queries that were submitted by thefirst user to the social networking system. For example, the first usermay have submitted search queries that reference a second user to viewthe second user's profile and/or content items that have been posted bythe second user. In this example, any content items posted by the seconduser can be included in the set of candidate content items. In someembodiments, the candidate generation module 204 identifies ascandidates any content items that were posted by users that werereferenced in one or more search queries that were submitted by athreshold number of users of the social networking system.

In some embodiments, the candidate generation module 204 identifies ascandidates any content items that were posted by users located in ageographic region (e.g., a point of interest, city, zip code, state,country, continent, geofence, etc.) in which the first user is alsolocated and/or has visited in the past. Such location information may beobtained, for example, from computing devices of users that are used toaccess the social networking system, metadata corresponding to contentitems that were posted by users, and/or information provided by users intheir respective social profiles in the social networking system, toname some examples. In some embodiments, the candidate generation module204 identifies as candidates any content items that were posted by usersthat are located in a geographic region in which users that are followedby the first user (e.g., friends of the first user) are located. Forexample, a content item posted by a third user can be included in theset of candidates if the third user is located in the same geographicregion as a second user that is followed by the first user.

In some embodiments, the candidate generation module 204 generates a newset of candidate content items when one or more criteria is satisfied.For example, the candidate generation module 204 can generate a new setof candidate content items at predetermined time intervals or after thefirst user has accessed, or viewed, a threshold number (e.g., 10 contentitems) of content items in the personalized content stream. In someembodiments, when generating a new set of candidate content items for auser, the candidate generation module 204 may discard content itemsincluded in the previous set of candidate content items that weredetermined for the user. In some embodiments, content items included inthe previous set of candidate content items are evaluated with respectto any new candidate content items to identify the best scoring contentitems to be included in the user's personalized content stream.

The filtering module 206 can be configured to refine the set ofcandidate content items by removing any content items that satisfycertain filtering criteria. For example, in some embodiments, contentitems included in the set may be restricted to a certain type of contentitem (e.g., videos). In such embodiments, any content items that do notmatch this type (e.g., images) are removed from the set of candidatecontent items. In some embodiments, a machine learning model can betrained to predict whether a content item should be removed from the setof candidate content items. In some embodiments, the model can betrained using training examples that each reference content items thathave been hand labeled by quality control personnel as bad content thatshould be excluded. Once trained, the model can then predict whether agiven content item should be excluded from the set of candidate contentitems, for example, based on the subject matter reflected in the contentitem. For example, a content item may be excluded if the content itemincludes objectionable content (e.g., nudity, violence, etc.) or thecontent item is likely to be reported as being inappropriate. In anotherexample, a content item may be excluded if a threshold number of usershave skipped viewing the content item and/or have reported the contentitem as being inappropriate. A content item can also be excluded if thecontent item was posted from a user account that has previously beendetermined to post bad content, for example.

In some embodiments, the model can be trained using feedback collectedfrom users during their interactions with content items in the socialnetworking system. For example, content items may be classified into oneor more categories (e.g., games, news, comedy, film, travel, sports,music, etc.) and/or sub-categories using generally known contentclassification techniques (e.g., subject matter classification). In suchembodiments, user feedback (e.g., likes, dislikes or skips, etc.) forcontent items can be collected and used to train the model to predictlikelihoods of a user “liking” a content item, of the user skippingplayback of the content item, and/or of the user discontinuing theviewing of a personalized content stream in which the content item isincluded. The filtering module 206 may exclude a content item if any ofthese likelihoods satisfy a threshold value.

After filtering, the remaining content items in the set of candidatecontent items can be scored and ranked for presentation in thepersonalized content stream. In various embodiments, the ranking module208 can score each content item in the set with respect to the firstuser using one or more trained machine learning models. In someembodiments, a model can be trained to predict a likelihood that thefirst user will “like” a given content item. For example, the model canbe trained using feedback collected from the first user with respect tovarious content items and their respective classifications, as describedabove. In some embodiments, the software application running on thefirst user's computing device through which the social networking systemis accessed may be configured to send information describing whichcontent items the first user has viewed, a respective view duration foreach content item, and/or a sequence in which the content items wereviewed, to name some examples. Such information can be used to furthertrain the model as described below.

For example, in some embodiments, the model can be trained to predict alikelihood that the first user will continue to watch more content itemsincluded in the personalized content stream after viewing a givencontent item. In such embodiments, the training examples used to trainthe model can each reference a content item viewed by the first user andindicate whether the first user continued watching a threshold number ofcontent items that were subsequently presented to the first user in thepersonalized content stream. In some embodiments, the model can betrained to predict a likelihood that the first user will watch a givencontent item to completion. In such embodiments, the training examplesused to train the model can each reference a type and/or classificationfor a content item that was presented to the first user and indicatewhether the first user watched the content item to completion. Dependingon the implementation, a content item may be deemed as being watched tocompletion if the first user views the entire duration of the contentitem or views the content item for some threshold period of time (e.g.,3 seconds). In some embodiments, a view count associated with the firstuser is incremented after the first user has watched a content item tocompletion. In some embodiments, the first user may be associated withmultiple view counts that each correspond to a particular type and/orclassification of content item. In such embodiments, the view countincremented after the first user watches a content item to completioncorresponds to the type and/or classification of the content item.

In some embodiments, the model can be trained to predict a likelihoodthat the first user will watch the playback of a given content item fora duration that is longer than an average duration the first usertypically views content items (e.g., before discontinuing the playback,closing the software application, etc.). In such embodiments, thetraining examples used to train the model can each reference a typeand/or classification for a content item that was presented to the firstuser and indicate whether the first user watched the content item for aduration that is longer than the average duration. In some embodiments,the model can be trained to predict a likelihood that the user willwatch the playback of a given content item for a duration that is longerthan an average duration that other users viewed the content item.

In various embodiments, a content item can be scored with respect to thefirst user using any one of the approaches described above or anycombination thereof. When using multiple approaches to score a contentitem, the respective likelihoods that measure the first user's behaviorcan be combined (e.g., summed, multiplied, etc.) to produce an overallscore for the content item. In some embodiments, the respectivelikelihoods may be weighted differently, for example, by assigningrespective coefficients to the likelihoods.

In some embodiments, the model(s) can be trained to output likelihoodsthat are specific for a given user. That is, a model can output alikelihood that was predicted for a given user based on feedback and/orinformation corresponding to that user. In some embodiments, users areclassified into one or more groups and the model(s) are trained tooutput likelihoods that are group-specific. That is, a model can outputa likelihood that was predicted for a given user based on feedbackand/or information corresponding to a group of users to which the givenuser was assigned. In one example, users that have exhibited similarpatterns of interactions (e.g., users that follow similar users, usersthat like similar content items, etc.) in the social networking systemmay be included in the same group. In another example, users may begrouped together based on their age range, gender, life stage (e.g.,user is enrolled in high school, user is enrolled in a university, useris at some stage of their career, user is retired, etc.), sharedattributes among some proportion of users followed by the user (e.g.,users that tend to follow athletes, etc.), location, languagepreference, to name some examples.

Once content items in the set of candidate content items have beenscored, the ranking module 208 can rank the content items based on theirrespective scores. In some embodiments, the ranking module 208 selects athreshold number (e.g., 20) of the top scoring content items to beincluded in the personalized content stream for the first user. Thispersonalized content stream may be accessible to the user through aninterface as described below in reference to FIGS. 3 and 4A-C.

In general, the content items included in the personalized contentstream have been determined to be relevant to the first user. However,in an effort to improve consistency between content item transitions, insome embodiments, the continuity module 210 is configured to modify theorder in which content items in the personalized content stream arepresented. For example, the continuity module 210 can cluster contentitems in the personalized content stream using one or more criteria. Insome embodiments, content items having the same classification (e.g.,topic, category, sub-category, subject matter classification, visualtheme, etc.) can be included in the same cluster. In some embodiments,content items that were posted by users located in the same geographicregion or within some threshold distance of one another can be includedin the same cluster. In some embodiments, content items having similarsound characteristics (e.g., audio is within a threshold volume range,decibel range, frequency range, etc.) can be included in the samecluster. For example, content items that are associated with audio thatsatisfies a threshold sound level consistency can be included in thesame cluster. In some embodiments, content items having the same musicand/or song are included in the same cluster. In some embodiments,content items having similar music (e.g., same genre) are included inthe same cluster. In some embodiments, content items having similarmotion characteristics (e.g., having frame rates within a thresholdrange) can be included in the same cluster. In some embodiments, a firstcontent item and a second content item can be included in the samecluster if a threshold number of people continue to watch playback ofthe second content item after playback of the first content item ends.

The continuity module 210 can then determine the order in which theclusters of content items will be presented in the personalized contentstream. In some embodiments, the continuity module 210 determines theorder based on respective distance scores between the clusters. Adistance score between a first cluster and a second cluster may becomputed using any of the approaches described herein or any combinationthereof. In some embodiments, when computing a distance score between afirst cluster and a second cluster, the continuity module 210 canmeasure respective distances between one or more content items in thefirst cluster with respect to one or more content items in the secondcluster. For example, a distance score between a first cluster and asecond cluster may be determined based on a visual similarity betweencontent items in the first cluster and content items in the secondcluster. In another example, a distance score between a first clusterand a second cluster may be determined based on a language similarity(e.g., language spoken during playback of the content items, languagecorresponding to text shown during playback of the content item, etc.)between content items in the first cluster and content items in thesecond cluster. In another example, a distance score between a firstcluster and a second cluster may be determined based on a socialaffinity between users that posted content items in the first clusterand users that posted content items in the second cluster. The socialaffinity may be determined using a social graph that is managed by thesocial networking system, for example. In such embodiments, contentitems posted by users having a strong social affinity will be closer indistance.

The continuity module 210 can order the clusters based on the distancescores. For example, a distance score between a first cluster and asecond cluster may be lower than a distance score between the firstcluster and a third cluster. In this example, the personalized streamcan be ordered so content items in the second cluster are presentedafter playback of the content items in the first cluster.

In some embodiments, the music module 212 can be configured to identifybackground music, or songs, to be played during playback of thepersonalized content stream. For example, the music module 212 canidentify any songs that will be played during playback of the contentitems included in the personalized content stream. The music module 212can then select one or more of the identified songs to be played duringplayback of content items in the personalized content stream. In someembodiments, the songs to be played are selected randomly. In someembodiments, the songs to be played may be determined based on whetherthe first user will like the song(s). For example, these may be songsthat were written and/or performed by artists that are followed by thefirst user in the social networking system. In another example, thesongs to be played may have been written and/or performed by artiststhat are local to the geographic region in which the first user islocated. In some embodiments, the music module 212 can select one ormore songs to be played during playback of a cluster of content items.For example, the music module 212 can identify any songs that will beplayed during playback of the content items included in a cluster. Themusic module 212 can then select one or more of the identified songs tobe played during playback of content items in the cluster using any ofthe approaches described above. In some embodiments, when a song isbeing played during playback of the content items, all other soundassociated with the content items is muted. In some embodiments, soundassociated with the content items is played at a lower volume settingwhile the song being played during playback of the content items isplayed at a higher volume setting.

FIG. 3A illustrates an example 300 of an interface 304, according to anembodiment of the present disclosure. In this example, the interface 304is presented through a display screen of the computing device 302.Further, the interface 304 may be provided through an application (e.g.,a web browser, a social networking application, messenger application,etc.) running on the computing device 302 that is configured to interactwith a social networking system. The interface 304 includes a number ofdifferent options for accessing content through the social networkingsystem. In some embodiments, the interface 304 includes a first regionthrough which a user operating the computing device 302 can access apersonalized content stream 306 (e.g., “Videos You Might Like”). In someembodiments, the personalized content stream 306 begins playingautomatically in the first region as soon as the interface 304 isdisplayed. The interface 304 can also include a second region throughwhich a grid 308 of content items (e.g., content items 310) can beaccessed. For example, the user can select any of the content items inthe grid 308 to access the corresponding content item. In someembodiments, one or more different personalized content streams 312 thatwere generated for the user can be included as a selectable content itemin the grid 308. In some embodiments, upon selecting the option 306, thesoftware application can be configured to provide an immersive interface352 to allow full screen playback of the content items in thepersonalized content stream, as illustrated in the example of FIG. 3B.

FIG. 4A illustrates another example 400 of an interface 404, accordingto an embodiment of the present disclosure. In this example, theinterface 404 is presented through a display screen of a computingdevice 402. Further, the interface 404 may be provided through anapplication (e.g., a web browser, a social networking application,messenger application, etc.) running on the computing device 402 that isconfigured to interact with a social networking system. The interface404 includes a first region that presents a carousel 406 of contentitems that are available for playback. In FIG. 4A, the carousel 406 isshown referencing the personalized content stream 410. The carousel 406can include both personalized content streams as well as curated contentitems. The interface 404 can also include a second region through whicha grid 408 of content items can be accessed, as described above. Thecarousel 406 can cycle through different content items that areavailable for selection as illustrated in FIG. 4B. In FIG. 4B, thecarousel 406 is shown transitioning from referencing the personalizedcontent stream 410 to a curated content item 412. Once the transitioningis complete, the carousel 406 can reference just the curated contentitem 412 for selection, as illustrated in the example of FIG. 4C.

FIG. 5 illustrates an example method 500 for providing personalizedcontent, according to an embodiment of the present disclosure. It shouldbe appreciated that there can be additional, fewer, or alternative stepsperformed in similar or alternative orders, or in parallel, within thescope of the various embodiments discussed herein unless otherwisestated.

At block 502, a set of candidate content items is generated from aplurality of content items that are available in the social networkingsystem, wherein one or more of the candidate content items are to beincluded in a personalized content stream for a first user. At block504, a corresponding score for each of the candidate content items isgenerated with respect to the first user. At block 506, a first set ofcontent items is determined from the set of candidate content itemsbased at least in part on the respective scores, wherein content itemsin the first set are included in the personalized content stream.

FIG. 6 illustrates an example method 600 for reordering a personalizedcontent stream, according to an embodiment of the present disclosure. Itshould be appreciated that there can be additional, fewer, oralternative steps performed in similar or alternative orders, or inparallel, within the scope of the various embodiments discussed hereinunless otherwise stated.

At block 602, information describing a personalized content stream of afirst user is obtained. The personalized content stream can include aset of content items to be presented to the first user according to afirst ordering. At block 604, a second ordering for the set of contentitems is determined based on one or more criteria. The second orderingsatisfies at least one measure of consistency. At block 606, thepersonalized content stream is modified to correspond to the secondordering.

It is contemplated that there can be many other uses, applications,and/or variations associated with the various embodiments of the presentdisclosure. For example, in some cases, user can choose whether or notto opt-in to utilize the disclosed technology. The disclosed technologycan also ensure that various privacy settings and preferences aremaintained and can prevent private information from being divulged. Inanother example, various embodiments of the present disclosure canlearn, improve, and/or be refined over time.

Social Networking System—Example Implementation

FIG. 7 illustrates a network diagram of an example system 700 that canbe utilized in various scenarios, in accordance with an embodiment ofthe present disclosure. The system 700 includes one or more user devices710, one or more external systems 720, a social networking system (orservice) 730, and a network 750. In an embodiment, the social networkingservice, provider, and/or system discussed in connection with theembodiments described above may be implemented as the social networkingsystem 730. For purposes of illustration, the embodiment of the system700, shown by FIG. 7, includes a single external system 720 and a singleuser device 710. However, in other embodiments, the system 700 mayinclude more user devices 710 and/or more external systems 720. Incertain embodiments, the social networking system 730 is operated by asocial network provider, whereas the external systems 720 are separatefrom the social networking system 730 in that they may be operated bydifferent entities. In various embodiments, however, the socialnetworking system 730 and the external systems 720 operate inconjunction to provide social networking services to users (or members)of the social networking system 730. In this sense, the socialnetworking system 730 provides a platform or backbone, which othersystems, such as external systems 720, may use to provide socialnetworking services and functionalities to users across the Internet.

The user device 710 comprises one or more computing devices (or systems)that can receive input from a user and transmit and receive data via thenetwork 750. In one embodiment, the user device 710 is a conventionalcomputer system executing, for example, a Microsoft Windows compatibleoperating system (OS), Apple OS X, and/or a Linux distribution. Inanother embodiment, the user device 710 can be a computing device or adevice having computer functionality, such as a smart-phone, a tablet, apersonal digital assistant (PDA), a mobile telephone, a laptop computer,a wearable device (e.g., a pair of glasses, a watch, a bracelet, etc.),a camera, an appliance, etc. The user device 710 is configured tocommunicate via the network 750. The user device 710 can execute anapplication, for example, a browser application that allows a user ofthe user device 710 to interact with the social networking system 730.In another embodiment, the user device 710 interacts with the socialnetworking system 730 through an application programming interface (API)provided by the native operating system of the user device 710, such asiOS and ANDROID. The user device 710 is configured to communicate withthe external system 720 and the social networking system 730 via thenetwork 750, which may comprise any combination of local area and/orwide area networks, using wired and/or wireless communication systems.

In one embodiment, the network 750 uses standard communicationstechnologies and protocols. Thus, the network 750 can include linksusing technologies such as Ethernet, 802.11, worldwide interoperabilityfor microwave access (WiMAX), 3G, 4G, CDMA, GSM, LTE, digital subscriberline (DSL), etc. Similarly, the networking protocols used on the network750 can include multiprotocol label switching (MPLS), transmissioncontrol protocol/Internet protocol (TCP/IP), User Datagram Protocol(UDP), hypertext transport protocol (HTTP), simple mail transferprotocol (SMTP), file transfer protocol (FTP), and the like. The dataexchanged over the network 750 can be represented using technologiesand/or formats including hypertext markup language (HTML) and extensiblemarkup language (XML). In addition, all or some links can be encryptedusing conventional encryption technologies such as secure sockets layer(SSL), transport layer security (TLS), and Internet Protocol security(IPsec).

In one embodiment, the user device 710 may display content from theexternal system 720 and/or from the social networking system 730 byprocessing a markup language document 714 received from the externalsystem 720 and from the social networking system 730 using a browserapplication 712. The markup language document 714 identifies content andone or more instructions describing formatting or presentation of thecontent. By executing the instructions included in the markup languagedocument 714, the browser application 712 displays the identifiedcontent using the format or presentation described by the markuplanguage document 714. For example, the markup language document 714includes instructions for generating and displaying a web page havingmultiple frames that include text and/or image data retrieved from theexternal system 720 and the social networking system 730. In variousembodiments, the markup language document 714 comprises a data fileincluding extensible markup language (XML) data, extensible hypertextmarkup language (XHTML) data, or other markup language data.Additionally, the markup language document 714 may include JavaScriptObject Notation (JSON) data, JSON with padding (JSONP), and JavaScriptdata to facilitate data-interchange between the external system 720 andthe user device 710. The browser application 712 on the user device 710may use a JavaScript compiler to decode the markup language document714.

The markup language document 714 may also include, or link to,applications or application frameworks such as FLASH™ or Unity™applications, the Silverlight™ application framework, etc.

In one embodiment, the user device 710 also includes one or more cookies716 including data indicating whether a user of the user device 710 islogged into the social networking system 730, which may enablemodification of the data communicated from the social networking system730 to the user device 710.

The external system 720 includes one or more web servers that includeone or more web pages 722 a, 722 b, which are communicated to the userdevice 710 using the network 750. The external system 720 is separatefrom the social networking system 730. For example, the external system720 is associated with a first domain, while the social networkingsystem 730 is associated with a separate social networking domain. Webpages 722 a, 722 b, included in the external system 720, comprise markuplanguage documents 714 identifying content and including instructionsspecifying formatting or presentation of the identified content. Asdiscussed previously, it should be appreciated that there can be manyvariations or other possibilities.

The social networking system 730 includes one or more computing devicesfor a social network, including a plurality of users, and providingusers of the social network with the ability to communicate and interactwith other users of the social network. In some instances, the socialnetwork can be represented by a graph, i.e., a data structure includingedges and nodes. Other data structures can also be used to represent thesocial network, including but not limited to databases, objects,classes, meta elements, files, or any other data structure. The socialnetworking system 730 may be administered, managed, or controlled by anoperator. The operator of the social networking system 730 may be ahuman being, an automated application, or a series of applications formanaging content, regulating policies, and collecting usage metricswithin the social networking system 730. Any type of operator may beused.

Users may join the social networking system 730 and then add connectionsto any number of other users of the social networking system 730 to whomthey desire to be connected. As used herein, the term “friend” refers toany other user of the social networking system 730 to whom a user hasformed a connection, association, or relationship via the socialnetworking system 730. For example, in an embodiment, if users in thesocial networking system 730 are represented as nodes in the socialgraph, the term “friend” can refer to an edge formed between anddirectly connecting two user nodes.

Connections may be added explicitly by a user or may be automaticallycreated by the social networking system 730 based on commoncharacteristics of the users (e.g., users who are alumni of the sameeducational institution). For example, a first user specifically selectsa particular other user to be a friend. Connections in the socialnetworking system 730 are usually in both directions, but need not be,so the terms “user” and “friend” depend on the frame of reference.Connections between users of the social networking system 730 areusually bilateral (“two-way”), or “mutual,” but connections may also beunilateral, or “one-way.” For example, if Bob and Joe are both users ofthe social networking system 730 and connected to each other, Bob andJoe are each other's connections. If, on the other hand, Bob wishes toconnect to Joe to view data communicated to the social networking system730 by Joe, but Joe does not wish to form a mutual connection, aunilateral connection may be established. The connection between usersmay be a direct connection; however, some embodiments of the socialnetworking system 730 allow the connection to be indirect via one ormore levels of connections or degrees of separation.

In addition to establishing and maintaining connections between usersand allowing interactions between users, the social networking system730 provides users with the ability to take actions on various types ofitems supported by the social networking system 730. These items mayinclude groups or networks (i.e., social networks of people, entities,and concepts) to which users of the social networking system 730 maybelong, events or calendar entries in which a user might be interested,computer-based applications that a user may use via the socialnetworking system 730, transactions that allow users to buy or sellitems via services provided by or through the social networking system730, and interactions with advertisements that a user may perform on oroff the social networking system 730. These are just a few examples ofthe items upon which a user may act on the social networking system 730,and many others are possible. A user may interact with anything that iscapable of being represented in the social networking system 730 or inthe external system 720, separate from the social networking system 730,or coupled to the social networking system 730 via the network 750.

The social networking system 730 is also capable of linking a variety ofentities. For example, the social networking system 730 enables users tointeract with each other as well as external systems 720 or otherentities through an API, a web service, or other communication channels.The social networking system 730 generates and maintains the “socialgraph” comprising a plurality of nodes interconnected by a plurality ofedges. Each node in the social graph may represent an entity that canact on another node and/or that can be acted on by another node. Thesocial graph may include various types of nodes. Examples of types ofnodes include users, non-person entities, content items, web pages,groups, activities, messages, concepts, and any other things that can berepresented by an object in the social networking system 730. An edgebetween two nodes in the social graph may represent a particular kind ofconnection, or association, between the two nodes, which may result fromnode relationships or from an action that was performed by one of thenodes on the other node. In some cases, the edges between nodes can beweighted. The weight of an edge can represent an attribute associatedwith the edge, such as a strength of the connection or associationbetween nodes. Different types of edges can be provided with differentweights. For example, an edge created when one user “likes” another usermay be given one weight, while an edge created when a user befriendsanother user may be given a different weight.

As an example, when a first user identifies a second user as a friend,an edge in the social graph is generated connecting a node representingthe first user and a second node representing the second user. Asvarious nodes relate or interact with each other, the social networkingsystem 730 modifies edges connecting the various nodes to reflect therelationships and interactions.

The social networking system 730 also includes user-generated content,which enhances a user's interactions with the social networking system730. User-generated content may include anything a user can add, upload,send, or “post” to the social networking system 730. For example, a usercommunicates posts to the social networking system 730 from a userdevice 710. Posts may include data such as status updates or othertextual data, location information, images such as photos, videos,links, music or other similar data and/or media. Content may also beadded to the social networking system 730 by a third party. Content“items” are represented as objects in the social networking system 730.In this way, users of the social networking system 730 are encouraged tocommunicate with each other by posting text and content items of varioustypes of media through various communication channels. Suchcommunication increases the interaction of users with each other andincreases the frequency with which users interact with the socialnetworking system 730.

The social networking system 730 includes a web server 732, an APIrequest server 734, a user profile store 736, a connection store 738, anaction logger 740, an activity log 742, and an authorization server 744.In an embodiment of the invention, the social networking system 730 mayinclude additional, fewer, or different components for variousapplications. Other components, such as network interfaces, securitymechanisms, load balancers, failover servers, management and networkoperations consoles, and the like are not shown so as to not obscure thedetails of the system.

The user profile store 736 maintains information about user accounts,including biographic, demographic, and other types of descriptiveinformation, such as work experience, educational history, hobbies orpreferences, location, and the like that has been declared by users orinferred by the social networking system 730. This information is storedin the user profile store 736 such that each user is uniquelyidentified. The social networking system 730 also stores data describingone or more connections between different users in the connection store738. The connection information may indicate users who have similar orcommon work experience, group memberships, hobbies, or educationalhistory. Additionally, the social networking system 730 includesuser-defined connections between different users, allowing users tospecify their relationships with other users. For example, user-definedconnections allow users to generate relationships with other users thatparallel the users' real-life relationships, such as friends,co-workers, partners, and so forth. Users may select from predefinedtypes of connections, or define their own connection types as needed.Connections with other nodes in the social networking system 730, suchas non-person entities, buckets, cluster centers, images, interests,pages, external systems, concepts, and the like are also stored in theconnection store 738.

The social networking system 730 maintains data about objects with whicha user may interact. To maintain this data, the user profile store 736and the connection store 738 store instances of the corresponding typeof objects maintained by the social networking system 730. Each objecttype has information fields that are suitable for storing informationappropriate to the type of object. For example, the user profile store736 contains data structures with fields suitable for describing auser's account and information related to a user's account. When a newobject of a particular type is created, the social networking system 730initializes a new data structure of the corresponding type, assigns aunique object identifier to it, and begins to add data to the object asneeded. This might occur, for example, when a user becomes a user of thesocial networking system 730, the social networking system 730 generatesa new instance of a user profile in the user profile store 736, assignsa unique identifier to the user account, and begins to populate thefields of the user account with information provided by the user.

The connection store 738 includes data structures suitable fordescribing a user's connections to other users, connections to externalsystems 720 or connections to other entities. The connection store 738may also associate a connection type with a user's connections, whichmay be used in conjunction with the user's privacy setting to regulateaccess to information about the user. In an embodiment of the invention,the user profile store 736 and the connection store 738 may beimplemented as a federated database.

Data stored in the connection store 738, the user profile store 736, andthe activity log 742 enables the social networking system 730 togenerate the social graph that uses nodes to identify various objectsand edges connecting nodes to identify relationships between differentobjects. For example, if a first user establishes a connection with asecond user in the social networking system 730, user accounts of thefirst user and the second user from the user profile store 736 may actas nodes in the social graph. The connection between the first user andthe second user stored by the connection store 738 is an edge betweenthe nodes associated with the first user and the second user. Continuingthis example, the second user may then send the first user a messagewithin the social networking system 730. The action of sending themessage, which may be stored, is another edge between the two nodes inthe social graph representing the first user and the second user.Additionally, the message itself may be identified and included in thesocial graph as another node connected to the nodes representing thefirst user and the second user.

In another example, a first user may tag a second user in an image thatis maintained by the social networking system 730 (or, alternatively, inan image maintained by another system outside of the social networkingsystem 730). The image may itself be represented as a node in the socialnetworking system 730. This tagging action may create edges between thefirst user and the second user as well as create an edge between each ofthe users and the image, which is also a node in the social graph. Inyet another example, if a user confirms attending an event, the user andthe event are nodes obtained from the user profile store 736, where theattendance of the event is an edge between the nodes that may beretrieved from the activity log 742. By generating and maintaining thesocial graph, the social networking system 730 includes data describingmany different types of objects and the interactions and connectionsamong those objects, providing a rich source of socially relevantinformation.

The web server 732 links the social networking system 730 to one or moreuser devices 710 and/or one or more external systems 720 via the network750. The web server 732 serves web pages, as well as other web-relatedcontent, such as Java, JavaScript, Flash, XML, and so forth. The webserver 732 may include a mail server or other messaging functionalityfor receiving and routing messages between the social networking system730 and one or more user devices 710. The messages can be instantmessages, queued messages (e.g., email), text and SMS messages, or anyother suitable messaging format.

The API request server 734 allows one or more external systems 720 anduser devices 710 to call access information from the social networkingsystem 730 by calling one or more API functions. The API request server734 may also allow external systems 720 to send information to thesocial networking system 730 by calling APIs. The external system 720,in one embodiment, sends an API request to the social networking system730 via the network 750, and the API request server 734 receives the APIrequest. The API request server 734 processes the request by calling anAPI associated with the API request to generate an appropriate response,which the API request server 734 communicates to the external system 720via the network 750. For example, responsive to an API request, the APIrequest server 734 collects data associated with a user, such as theuser's connections that have logged into the external system 720, andcommunicates the collected data to the external system 720. In anotherembodiment, the user device 710 communicates with the social networkingsystem 730 via APIs in the same manner as external systems 720.

The action logger 740 is capable of receiving communications from theweb server 732 about user actions on and/or off the social networkingsystem 730. The action logger 740 populates the activity log 742 withinformation about user actions, enabling the social networking system730 to discover various actions taken by its users within the socialnetworking system 730 and outside of the social networking system 730.Any action that a particular user takes with respect to another node onthe social networking system 730 may be associated with each user'saccount, through information maintained in the activity log 742 or in asimilar database or other data repository. Examples of actions taken bya user within the social networking system 730 that are identified andstored may include, for example, adding a connection to another user,sending a message to another user, reading a message from another user,viewing content associated with another user, attending an event postedby another user, posting an image, attempting to post an image, or otheractions interacting with another user or another object. When a usertakes an action within the social networking system 730, the action isrecorded in the activity log 742. In one embodiment, the socialnetworking system 730 maintains the activity log 742 as a database ofentries. When an action is taken within the social networking system730, an entry for the action is added to the activity log 742. Theactivity log 742 may be referred to as an action log.

Additionally, user actions may be associated with concepts and actionsthat occur within an entity outside of the social networking system 730,such as an external system 720 that is separate from the socialnetworking system 730. For example, the action logger 740 may receivedata describing a user's interaction with an external system 720 fromthe web server 732. In this example, the external system 720 reports auser's interaction according to structured actions and objects in thesocial graph.

Other examples of actions where a user interacts with an external system720 include a user expressing an interest in an external system 720 oranother entity, a user posting a comment to the social networking system730 that discusses an external system 720 or a web page 722 a within theexternal system 720, a user posting to the social networking system 730a Uniform Resource Locator (URL) or other identifier associated with anexternal system 720, a user attending an event associated with anexternal system 720, or any other action by a user that is related to anexternal system 720. Thus, the activity log 742 may include actionsdescribing interactions between a user of the social networking system730 and an external system 720 that is separate from the socialnetworking system 730.

The authorization server 744 enforces one or more privacy settings ofthe users of the social networking system 730. A privacy setting of auser determines how particular information associated with a user can beshared. The privacy setting comprises the specification of particularinformation associated with a user and the specification of the entityor entities with whom the information can be shared. Examples ofentities with which information can be shared may include other users,applications, external systems 720, or any entity that can potentiallyaccess the information. The information that can be shared by a usercomprises user account information, such as profile photos, phonenumbers associated with the user, user's connections, actions taken bythe user such as adding a connection, changing user profile information,and the like.

The privacy setting specification may be provided at different levels ofgranularity. For example, the privacy setting may identify specificinformation to be shared with other users; the privacy settingidentifies a work phone number or a specific set of related information,such as, personal information including profile photo, home phonenumber, and status. Alternatively, the privacy setting may apply to allthe information associated with the user. The specification of the setof entities that can access particular information can also be specifiedat various levels of granularity. Various sets of entities with whichinformation can be shared may include, for example, all friends of theuser, all friends of friends, all applications, or all external systems720. One embodiment allows the specification of the set of entities tocomprise an enumeration of entities. For example, the user may provide alist of external systems 720 that are allowed to access certaininformation. Another embodiment allows the specification to comprise aset of entities along with exceptions that are not allowed to access theinformation. For example, a user may allow all external systems 720 toaccess the user's work information, but specify a list of externalsystems 720 that are not allowed to access the work information. Certainembodiments call the list of exceptions that are not allowed to accesscertain information a “block list”. External systems 720 belonging to ablock list specified by a user are blocked from accessing theinformation specified in the privacy setting. Various combinations ofgranularity of specification of information, and granularity ofspecification of entities, with which information is shared arepossible. For example, all personal information may be shared withfriends whereas all work information may be shared with friends offriends.

The authorization server 744 contains logic to determine if certaininformation associated with a user can be accessed by a user's friends,external systems 720, and/or other applications and entities. Theexternal system 720 may need authorization from the authorization server744 to access the user's more private and sensitive information, such asthe user's work phone number. Based on the user's privacy settings, theauthorization server 744 determines if another user, the external system720, an application, or another entity is allowed to access informationassociated with the user, including information about actions taken bythe user.

In some embodiments, the social networking system 730 can include acontent provider module 746. The content provider module 746 can, forexample, be implemented as the content provider module 102 of FIG. 1. Insome embodiments, the content provider module 746, in whole or in part,may be implemented in a user device 710 or the external system 720. Asdiscussed previously, it should be appreciated that there can be manyvariations or other possibilities.

Hardware Implementation

The foregoing processes and features can be implemented by a widevariety of machine and computer system architectures and in a widevariety of network and computing environments. FIG. 8 illustrates anexample of a computer system 800 that may be used to implement one ormore of the embodiments described herein in accordance with anembodiment of the invention. The computer system 800 includes sets ofinstructions for causing the computer system 800 to perform theprocesses and features discussed herein. The computer system 800 may beconnected (e.g., networked) to other machines. In a networkeddeployment, the computer system 800 may operate in the capacity of aserver machine or a client machine in a client-server networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. In an embodiment of the invention, the computersystem 800 may be the social networking system 730, the user device 710,and the external system 820, or a component thereof. In an embodiment ofthe invention, the computer system 800 may be one server among many thatconstitutes all or part of the social networking system 730.

The computer system 800 includes a processor 802, a cache 804, and oneor more executable modules and drivers, stored on a computer-readablemedium, directed to the processes and features described herein.Additionally, the computer system 800 includes a high performanceinput/output (I/O) bus 806 and a standard I/O bus 808. A host bridge 810couples processor 802 to high performance I/O bus 806, whereas I/O busbridge 812 couples the two buses 806 and 808 to each other. A systemmemory 814 and one or more network interfaces 816 couple to highperformance I/O bus 806. The computer system 800 may further includevideo memory and a display device coupled to the video memory (notshown). Mass storage 818 and I/O ports 820 couple to the standard I/Obus 808. The computer system 800 may optionally include a keyboard andpointing device, a display device, or other input/output devices (notshown) coupled to the standard I/O bus 808. Collectively, these elementsare intended to represent a broad category of computer hardware systems,including but not limited to computer systems based on thex86-compatible processors manufactured by Intel Corporation of SantaClara, Calif., and the x86-compatible processors manufactured byAdvanced Micro Devices (AMD), Inc., of Sunnyvale, Calif., as well as anyother suitable processor.

An operating system manages and controls the operation of the computersystem 800, including the input and output of data to and from softwareapplications (not shown). The operating system provides an interfacebetween the software applications being executed on the system and thehardware components of the system. Any suitable operating system may beused, such as the LINUX Operating System, the Apple Macintosh OperatingSystem, available from Apple Computer Inc. of Cupertino, Calif., UNIXoperating systems, Microsoft® Windows® operating systems, BSD operatingsystems, and the like. Other implementations are possible.

The elements of the computer system 800 are described in greater detailbelow. In particular, the network interface 816 provides communicationbetween the computer system 800 and any of a wide range of networks,such as an Ethernet (e.g., IEEE 802.3) network, a backplane, etc. Themass storage 818 provides permanent storage for the data and programminginstructions to perform the above-described processes and featuresimplemented by the respective computing systems identified above,whereas the system memory 814 (e.g., DRAM) provides temporary storagefor the data and programming instructions when executed by the processor802. The I/O ports 820 may be one or more serial and/or parallelcommunication ports that provide communication between additionalperipheral devices, which may be coupled to the computer system 800.

The computer system 800 may include a variety of system architectures,and various components of the computer system 800 may be rearranged. Forexample, the cache 804 may be on-chip with processor 802. Alternatively,the cache 804 and the processor 802 may be packed together as a“processor module”, with processor 802 being referred to as the“processor core”. Furthermore, certain embodiments of the invention mayneither require nor include all of the above components. For example,peripheral devices coupled to the standard I/O bus 808 may couple to thehigh performance I/O bus 806. In addition, in some embodiments, only asingle bus may exist, with the components of the computer system 800being coupled to the single bus. Moreover, the computer system 800 mayinclude additional components, such as additional processors, storagedevices, or memories.

In general, the processes and features described herein may beimplemented as part of an operating system or a specific application,component, program, object, module, or series of instructions referredto as “programs”. For example, one or more programs may be used toexecute specific processes described herein. The programs typicallycomprise one or more instructions in various memory and storage devicesin the computer system 800 that, when read and executed by one or moreprocessors, cause the computer system 800 to perform operations toexecute the processes and features described herein. The processes andfeatures described herein may be implemented in software, firmware,hardware (e.g., an application specific integrated circuit), or anycombination thereof.

In one implementation, the processes and features described herein areimplemented as a series of executable modules run by the computer system800, individually or collectively in a distributed computingenvironment. The foregoing modules may be realized by hardware,executable modules stored on a computer-readable medium (ormachine-readable medium), or a combination of both. For example, themodules may comprise a plurality or series of instructions to beexecuted by a processor in a hardware system, such as the processor 802.Initially, the series of instructions may be stored on a storage device,such as the mass storage 818. However, the series of instructions can bestored on any suitable computer readable storage medium. Furthermore,the series of instructions need not be stored locally, and could bereceived from a remote storage device, such as a server on a network,via the network interface 816. The instructions are copied from thestorage device, such as the mass storage 818, into the system memory 814and then accessed and executed by the processor 802. In variousimplementations, a module or modules can be executed by a processor ormultiple processors in one or multiple locations, such as multipleservers in a parallel processing environment.

Examples of computer-readable media include, but are not limited to,recordable type media such as volatile and non-volatile memory devices;solid state memories; floppy and other removable disks; hard diskdrives; magnetic media; optical disks (e.g., Compact Disk Read-OnlyMemory (CD ROMS), Digital Versatile Disks (DVDs)); other similarnon-transitory (or transitory), tangible (or non-tangible) storagemedium; or any type of medium suitable for storing, encoding, orcarrying a series of instructions for execution by the computer system800 to perform any one or more of the processes and features describedherein.

For purposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of the description. It will beapparent, however, to one skilled in the art that embodiments of thedisclosure can be practiced without these specific details. In someinstances, modules, structures, processes, features, and devices areshown in block diagram form in order to avoid obscuring the description.In other instances, functional block diagrams and flow diagrams areshown to represent data and logic flows. The components of blockdiagrams and flow diagrams (e.g., modules, blocks, structures, devices,features, etc.) may be variously combined, separated, removed,reordered, and replaced in a manner other than as expressly describedand depicted herein.

Reference in this specification to “one embodiment”, “an embodiment”,“other embodiments”, “one series of embodiments”, “some embodiments”,“various embodiments”, or the like means that a particular feature,design, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of, for example, the phrase “in one embodiment” or “in anembodiment” in various places in the specification are not necessarilyall referring to the same embodiment, nor are separate or alternativeembodiments mutually exclusive of other embodiments. Moreover, whetheror not there is express reference to an “embodiment” or the like,various features are described, which may be variously combined andincluded in some embodiments, but also variously omitted in otherembodiments. Similarly, various features are described that may bepreferences or requirements for some embodiments, but not otherembodiments.

The language used herein has been principally selected for readabilityand instructional purposes, and it may not have been selected todelineate or circumscribe the inventive subject matter. It is thereforeintended that the scope of the invention be limited not by this detaileddescription, but rather by any claims that issue on an application basedhereon. Accordingly, the disclosure of the embodiments of the inventionis intended to be illustrative, but not limiting, of the scope of theinvention, which is set forth in the following claims.

What is claimed is:
 1. A computer-implemented method comprising:generating, by a social networking system, a set of candidate contentitems from a plurality of content items that are available in the socialnetworking system, wherein one or more of the candidate content itemsare to be included in a personalized content stream for a first user;generating, by the social networking system, a corresponding score foreach of the candidate content items with respect to the first user; anddetermining, by the social networking system, a first set of contentitems from the set of candidate content items based at least in part onthe respective scores, wherein content items in the first set areincluded in the personalized content stream.
 2. The computer-implementedmethod of claim 1, wherein generating a respective score for a candidatecontent item further comprises: determining, by the social networkingsystem, a likelihood of the first user selecting an option to like acandidate content item through the social networking system, thelikelihood being determined using a trained machine learning model,wherein the score for the candidate content item is based at least inpart on the likelihood.
 3. The computer-implemented method of claim 1,wherein generating a respective score for a candidate content itemfurther comprises: determining, by the social networking system, alikelihood of the first user watching one or more additional contentitems after having viewed a candidate content item, the likelihood beingdetermined using a trained machine learning model, wherein the score forthe candidate content item is based at least in part on the likelihood.4. The computer-implemented method of claim 1, wherein generating arespective score for a candidate content item further comprises:determining, by the social networking system, a likelihood of the firstuser watching a candidate content item to completion, the likelihoodbeing determined using a trained machine learning model, wherein thescore for the candidate content item is based at least in part on thelikelihood.
 5. The computer-implemented method of claim 1, whereingenerating a respective score for a candidate content item furthercomprises: determining, by the social networking system, a likelihood ofthe first user watching a playback of a candidate content item for aduration of time that is longer than an average duration of time thefirst user watches playback of content items, the likelihood beingdetermined using a trained machine learning model, wherein the score forthe candidate content item is based at least in part on the likelihood.6. The computer-implemented method of claim 1, wherein generating arespective score for a candidate content item further comprises:determining, by the social networking system, a likelihood of the firstuser watching a playback of a candidate content item for a duration oftime that is longer than an average duration of time that other userswatched playbacks of the candidate content item, the likelihood beingdetermined using a trained machine learning model, wherein the score forthe candidate content item is based at least in part on the likelihood.7. The computer-implemented method of claim 1, wherein generating theset of candidate content items further comprises: obtaining, by thesocial networking system, one or more content items that were liked byat least one second user that the first user is following in the socialnetworking system.
 8. The computer-implemented method of claim 1,wherein generating the set of candidate content items further comprises:determining, by the social networking system, that the first user haspreviously liked one or more content items that were posted by at leastone second user; and obtaining, by the social networking system, one ormore content items that were liked by the second user.
 9. Thecomputer-implemented method of claim 1, wherein generating the set ofcandidate content items further comprises: obtaining, by the socialnetworking system, one or more content items that were posted by usersthat are located in a geographic region in which the first user is alsolocated or has visited.
 10. The computer-implemented method of claim 1,the method further comprising: filtering, by the social networkingsystem, the set of candidate content items to exclude content items thatare likely to be flagged by users as being inappropriate or contentitems that were posted by users that have previously been flagged asposters of inappropriate content.
 11. A system comprising: at least oneprocessor; and a memory storing instructions that, when executed by theat least one processor, cause the system to perform: generating a set ofcandidate content items from a plurality of content items that areavailable in the social networking system, wherein one or more of thecandidate content items are to be included in a personalized contentstream for a first user; generating a corresponding score for each ofthe candidate content items with respect to the first user; anddetermining a first set of content items from the set of candidatecontent items based at least in part on the respective scores, whereincontent items in the first set are included in the personalized contentstream.
 12. The system of claim 11, wherein generating a respectivescore for a candidate content item further causes the system to perform:determining a likelihood of the first user selecting an option to like acandidate content item through the social networking system, thelikelihood being determined using a trained machine learning model,wherein the score for the candidate content item is based at least inpart on the likelihood.
 13. The system of claim 11, wherein generating arespective score for a candidate content item further causes the systemto perform: determining a likelihood of the first user watching one ormore additional content items after having viewed a candidate contentitem, the likelihood being determined using a trained machine learningmodel, wherein the score for the candidate content item is based atleast in part on the likelihood.
 14. The system of claim 11, whereingenerating a respective score for a candidate content item furthercauses the system to perform: determining a likelihood of the first userwatching a candidate content item to completion, the likelihood beingdetermined using a trained machine learning model, wherein the score forthe candidate content item is based at least in part on the likelihood.15. The system of claim 11, wherein generating a respective score for acandidate content item further causes the system to perform: determininga likelihood of the first user watching a playback of a candidatecontent item for a duration of time that is longer than an averageduration of time the first user watches playback of content items, thelikelihood being determined using a trained machine learning model,wherein the score for the candidate content item is based at least inpart on the likelihood.
 16. A non-transitory computer-readable storagemedium including instructions that, when executed by at least oneprocessor of a computing system, cause the computing system to perform amethod comprising: generating a set of candidate content items from aplurality of content items that are available in the social networkingsystem, wherein one or more of the candidate content items are to beincluded in a personalized content stream for a first user; generating acorresponding score for each of the candidate content items with respectto the first user; and determining a first set of content items from theset of candidate content items based at least in part on the respectivescores, wherein content items in the first set are included in thepersonalized content stream.
 17. The non-transitory computer-readablestorage medium of claim 16, wherein generating a respective score for acandidate content item further causes the computing system to perform:determining a likelihood of the first user selecting an option to like acandidate content item through the social networking system, thelikelihood being determined using a trained machine learning model,wherein the score for the candidate content item is based at least inpart on the likelihood.
 18. The non-transitory computer-readable storagemedium of claim 16, wherein generating a respective score for acandidate content item further causes the computing system to perform:determining a likelihood of the first user watching one or moreadditional content items after having viewed a candidate content item,the likelihood being determined using a trained machine learning model,wherein the score for the candidate content item is based at least inpart on the likelihood.
 19. The non-transitory computer-readable storagemedium of claim 16, wherein generating a respective score for acandidate content item further causes the computing system to perform:determining a likelihood of the first user watching a candidate contentitem to completion, the likelihood being determined using a trainedmachine learning model, wherein the score for the candidate content itemis based at least in part on the likelihood.
 20. The non-transitorycomputer-readable storage medium of claim 16, wherein generating arespective score for a candidate content item further causes thecomputing system to perform: determining a likelihood of the first userwatching a playback of a candidate content item for a duration of timethat is longer than an average duration of time the first user watchesplayback of content items, the likelihood being determined using atrained machine learning model, wherein the score for the candidatecontent item is based at least in part on the likelihood.